The impact of climate variability on soybean yields in Argentina. Multivariate regression

Abstract

Climate variability is examined and discussed in this work, emphasizing its influence over the fluctuation of soybean yield in the Pampas (central-eastern Argentina). Monthly data of rainfall, maximum and minimum temperatures, thermal range and seasonal rainfall were analysed jointly with the soybean yield in the period 1973-2000. Low-frequency variability was significant only in the minimum temperature during November in almost all the stations. This situation is favourable to the crop since during this month, seed germination, a growth stage sensitive to low temperatures, takes place. In the crop's core production region, 72% of the series of soybean yield presented a positive trend. Except in years with extreme rainfall situations, interannual variability of the soybean yield is in phase with the seasonal rainfall interannual variability. During these years, losses in the soybean crop occurred, with yield negative anomalies greater than one standard deviation. Soybean yield showed spatial coherence at the local scale, except in the crop's core zone. The association between each climate variable and yield did not show a defined regional pattern. Summer high temperature and rainfall excesses during the period of maturity and harvest have the greatest negative impact on the crop, whilst higher minimum temperatures during the growing season favour high yields. The joint effect of climate variables over yield was studied with multivariate statistical models, assuming that the effect of other factors (such as soil, technology, pests) is contained in the residuals. The regression models represent the estimates of the yield satisfactorily (high percentage of explained variance) and can be used to assess expected anomalies of mean soybean yield for a particular year. However, the predictor variables of the yield depend on the region.

ENSO impacts on Atlantic watersheds of South America

“…Anomalous monthly rainfalls were also recorded in Rocha, Uruguay, in 1941Uruguay, in , 1959Uruguay, in , 1986Uruguay, in and 1998 and recorded in the sediments of the Blanca shallow lake (García-Rodríguez et al 2002). Interannual precipitation variability associated with ENSOs had different consequences for soybean yields depending on crop growth-stage (Podestá et al 1999, Peñalba et al 2007. Some basins without discharge to the ocean in Central Argentina, located in the Arid South American Diagonal, are particularly subject to water excesses delivered during ENSOs .…”

Effects of climate variation on winter cereal production in Spain

“…The comparison between correlation maps of CP with climatic fields and the spatial variation modes or EOFs allow us to select the potential predictors that capture the CP response to climate variability. Landau et al (2000) developed a statistical model to represent the effects of weather on wheat yield in the UK; Alexandrov & Hoogenboom (2000) applied regression models to describe the relationship between crop yield, precipitation and air temperature; Penalba et al (2007) examined the influence of climate variability on the fluctuation of soybean yield in Argentina by using the multivariate regression method. We applied linear regression models to obtain the cereal yield response to climate variables as follows.…”

Soybean yield modeling using bootstrap methods for small samples

“…When modeling soybean yield, variables concerning agricultural meteorology (Penalba et al, 2007;Tao et al, 2008), agriculture (Zheng et al, 2009), management (Lobell et al, 2 ymptotic method in small samples. Although bootstrap is a well-known technique and is frequently employed in agricultural studies -as seen in works by Sabaghnia et al (2010), García-Gallego et al (2015), Losada et al (2015) and Sutton et al (2016) -the development of statistical and computing models has led to the study of new techniques based on the bootstrap method.…”